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To analyze a PDF with Ollama, first use a document tool to extract its text—or OCR scanned pages—then send relevant content to an Ollama model. Open WebUI can handle PDF extraction and let you either attach a file to a chat or create a reusable document collection. Check the extracted text before trusting answers, especially when the PDF contains scans, tables, diagrams, or many pages.
Can Ollama read PDFs directly?
Ollama’s documented vision interface accepts images: a vision-capable model can describe, classify, and answer questions about an image supplied alongside text. That is different from parsing a PDF file. The Ollama vision documentation demonstrates image input, while Open WebUI documents PDF extraction and retrieval as separate steps. In a typical workflow, an application extracts PDF text or renders pages before a model analyzes the material.
This distinction matters because a text-based PDF, a scanned page, and a diagram-heavy document need not follow the same path. A model cannot answer reliably about content that the extraction or retrieval process never supplied to it.
Choose a workflow for one PDF or a collection
| Workflow | Best fit | How it works |
|---|---|---|
| Attach a PDF to a chat | A single document or an occasional question | Open WebUI chunks and embeds the attachment for that chat, then retrieves relevant content as you ask questions. Open WebUI Essentials |
| Create a knowledge base | Documents you expect to reuse across chats | Open WebUI stores documents as chunks with embeddings and retrieves relevant pieces at question time. Open WebUI RAG documentation |
These are workflow options, not a performance comparison. For a one-time question, attach the file. For recurring questions across a set of documents, a knowledge base avoids reintroducing each file in every chat.
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Analyze a single PDF in Open WebUI
- Connect a running Ollama model. Configure Open WebUI to use your Ollama instance and select a model available in that setup.
- Attach the PDF or add it to Documents. For a one-off task, attach it to a chat. For a document you will reuse, add it to a knowledge base. Open WebUI’s Essentials guide describes both options.
- Ask a focused question. For example: “What does the termination section say about notice? Quote the relevant passage and identify its page or section.” Requesting evidence makes it easier to check the response.
- Verify the passage in the PDF. Treat the model’s answer as a navigation and analysis aid, not as validated fact. Confirm important dates, obligations, calculations, and quotations against the source file.
- Inspect extraction if the answer is incomplete or wrong. Preview the extracted text and check whether the relevant passage appears there before changing models or rephrasing the prompt. Open WebUI’s troubleshooting guidance recommends checking extraction output when content is missing.
What changes for scans, tables, and diagrams?
Scanned PDFs may need OCR
A text-based PDF often exposes selectable text to an extractor. A scan may contain only page images, so OCR is needed to turn the page into searchable text. Open WebUI documents extraction for text-based and scanned PDFs, but does not guarantee perfect results for every file or extraction engine. If text is blank or incomplete, inspect the extracted output and try an extraction route suitable for the document. Its documentation lists multiple engines and explains that the default path uses pypdf; it suggests considering Tika or Docling beyond casual use. Document extraction details
Tables and complex layouts need checking
Extraction can lose relationships between values, headings, and columns. Check the extracted version of a table against the page layout before asking for calculations or comparisons. If the model’s answer depends on a particular row, column, footnote, or label, verify that the extracted text preserves that association.
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Visual content requires a path that actually supplies images
Ollama’s image input does not automatically mean an application sends every PDF page to a vision model. Open WebUI documents extraction engines and configurable media handling; the workflow must actually expose page images to OCR or vision processing when diagrams, figures, or visual layout matter. Ollama’s GLM-OCR model page describes image-oriented recognition examples for text, tables, and figures. It is one option to explore, not a guarantee of accurate results or an automatic end-to-end PDF converter.
Use a knowledge base for documents you revisit
Retrieval-augmented generation (RAG) makes a collection searchable without necessarily placing every page into every prompt. Open WebUI describes a process in which documents are split into chunks, converted into embeddings, stored, and then searched for relevant passages when you ask a question. The selected chunks—not necessarily the whole PDF—are what the model uses to answer. Open WebUI RAG documentation
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For better results, ask questions that identify the subject or section you need. If an answer omits a detail, check whether retrieval returned the passage containing it. A relevant passage can still be left out if retrieval fails to find it or the available context cannot accommodate it.
Understand context limits on long PDFs
The model’s context window limits how much text can be considered in a request. Open WebUI says Ollama selects a default context length based on available GPU VRAM; its RAG documentation states that GPUs with less than 24 GiB default to 4096 tokens and recommends increasing context for larger workloads, subject to model support. These are Open WebUI’s documented defaults, not fixed behavior for every Ollama installation or configuration. RAG context guidance
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For a long file, ask targeted questions rather than expecting one prompt to cover everything. If answers miss relevant material, check extraction first, then retrieval, and then whether the context setting is appropriate for the model and hardware. Increasing context does not remove the need to verify the source passage.
Troubleshoot missing or unreliable answers
- The answer ignores a section: Preview extracted text and confirm the section is present. If it is missing, adjust or change the extraction engine before retrying. Open WebUI troubleshooting
- The PDF is a scan: Use an OCR-capable extraction route and confirm that the text output is readable and complete. Open WebUI extraction documentation
- A knowledge-base answer lacks evidence: Check whether the relevant chunk was retrieved; retrieval does not guarantee that every page or passage is included. Open WebUI RAG documentation
- Retrieval worsened after changing embedding models: Re-embed the documents with the new embedding model. Open WebUI warns that mismatched or stale embeddings can harm retrieval. Open WebUI troubleshooting
- A table or figure is misread: Compare the answer with the PDF’s visual page. Use a workflow that actually sends page imagery to an appropriate OCR or vision step when text extraction is insufficient.
What local processing does—and does not—mean
Running an Ollama model locally does not by itself prove that every step is local. PDF extraction, OCR, embeddings, storage, and the model endpoint may follow different routes depending on how Open WebUI and its services are configured. Open WebUI says Temporary Chat performs document extraction exclusively in the browser to avoid backend storage or processing, while noting that complex formats dependent on backend parsers may not work correctly. Check your deployment and extraction path before relying on a privacy assumption. Open WebUI document handling · Open WebUI RAG documentation
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